An SMT-Based Approach to Motion Planning for Multiple Robots With Complex Constraints
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, we propose a new method for solving multirobot motion planning problems with complex constraints. We focus on an important class of problems that require an allocation of spatially distributed tasks to robots, along with efficient paths for each robot to visits their task locations. We introduce a framework for solving these problems that naturally couples allocation with path planning. The allocation problem is encoded as a Boolean Satisfiability problem (SAT) and the path planning problem is encoded as a traveling salesman problem (TSP). In addition, the framework can handle complex constraints such as battery life limitations, robot carrying capacities, and robot-task incompatibilities. We propose an algorithm that leverages recent advances in Satisfiability Modulo Theory (SMT) to combine state-of-the-art SAT and TSP solvers. We characterize the correctness of our algorithm and evaluate it in simulation on a series of patrolling, periodic routing, and multirobot sample collection problems. The results show our algorithm significantly outperforms state-of-the-art mathematical programming solvers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it